[Artificial intelligence in the diagnosis and management of aortic disease: current applications and future...

Artificial intelligence (AI) is rapidly transforming the field of aortic imaging, enhancing diagnostic accuracy, risk stratification, and treatment planning. This review provides a comprehensive overview of AI applications in measuring aortic dimensions, detecting and characterizing aneurysms, dissections, and atherosclerotic disease, as well as predicting clinical outcomes. Automated measurement and segmentation tools, powered by deep learning algorithms, offer reproducible and time-efficient assessments, reducing inter- and intraobserver variability. In atherosclerotic disease, AI enables objective quantification of calcification burden and advanced radiomic analysis for prognostic stratification. In acute aortic syndromes, AI-based models improve diagnostic sensitivity, assist in differentiating true from false lumens, and predict complications or surgical outcomes. The integration of emerging technologies such as radiomics, dual-energy computed tomography, photon-counting computed tomography, and computational fluid dynamics further expands predictive capabilities, potentially leading to personalized "digital twin" models for therapeutic decision-making. Despite promising results, challenges remain in software availability, cost, data integration, and defining the radiologist's evolving role. AI holds the potential to become an indispensable tool in aortic disease management, bridging imaging, clinical, and computational domains to improve patient outcomes.